In a general COVID-19 population in Cox’s Bazar, Bangladesh, we developed a medication recommendation system based on clinical information from the electronic medical record (EMR). Our goal was also to enable deep learning (DL) strategies to quickly assist physicians and COVID-19 patients by recommending necessary medications. The general demographic data, clinical symptoms, basic clinical tests, and drug information of 8953 patients were used to create a dataset. The learning model in this COVID-MED model was created using Keras (an open-source artificial neural network library) to solve regression problems. In this study, a sequential model was adopted. In order to improve the prediction capability and achieve global minima quickly and smoothly, the COVID-MED model incorporates an adaptive optimizer dubbed Adam. The model calculated a mean absolute error of 0.0037, a mean squared error of 0.000035, and a root mean squared error of 0.0059. The model predicts the output medications, such as injections or other oral medications, with around 99% accuracy. These findings show that medication can be predicted using information from the EMR. Similar models allow for patient-specific decision support to help prevent medication errors in diseases other than COVID-19.
Since the globe has faced extreme difficulties with COVID-19, Artificial Intelligence appeared to help to cope with this epidemic in an innumerable number of ways. Motivated by this, in this article, a robust prediction model called COVID-SDL has been proposed using Sequential Deep Learning (SDL) for predicting the total positive cases per day. In order to evaluate the performance of COVID-SDL, data samples used in the model have been collected from Italy’s COVID-19 situation reports. Besides this, the dataset has gone through the processes of cleaning, filtering, formatting and visualization. COVID-SDL utilizes the correlation information among the features that have strengthened the prediction capability. Also, the exploratory survey showed that 5 most salient features (Home Confinement, Deaths, Recovered, Current Positive Cases and Tests Performed) results better which is obtained from the mentioned dataset primarily composed of 17 features. In addition, to assist the prediction ability of COVID-SDL, ReLu (Rectified Linear Unit) activation function has been used which enhanced the robustness of the model. With a view to making the predictions highly accurate, Adam optimizer has been adopted which works by reducing the cost function and making further updates of the weights. Moreover, COVID-SDL has successfully obtained accuracy parameters such as MAE- 0.00037316, MSE- 0.00000018, RMSE- 0.00043476 and R2 Score- 0.99999 with providing the best fit curve of predicted data which covers 99.999% of the actual data. Furthermore, to prove the robustness of the COVID-SDL, a comparative test among the adaptive and non-adaptive optimizers has also been performed.
This paper proposes a Solar Radiation Prediction Model employing Ant Colony Optimization (ACO) and Artificial Neural Network (ANN), named as SRPM. SRPM aims to incorporate the Feature Selection (FS) technique in the ANN training with the guidance of hybridizing ACO. The main reason behind using FS technique is, it can provide an improved solution from a particular problem by identifying the most salient features from the available feature set. In SRPM, the hybridizing ACO search technique utilizes the information gathered from the correlation among the features and the result of ANN training. To assist the ACO search, pheromone updating technique and measurement of heuristic information have been performed by two particular sets of rules. Thus, SRPM utilizes the benefits of using the wrapper and filter approaches in selecting the salient features during SRP task. The combination eventually creates an equipoise between exploration and exploitation of ants in the way of searching as well as strengthening the capability of global search of ACO for obtaining well qualified solution in SRPM. To evaluate the executive efficiency of SRPM, data samples were collected from BMD and NASA-SSE department. Exploratory outcomes show that SRP can select six utmost salient features by providing 99.74% and 99.78% averaged testing accuracies for BMD and NASA-SSE data samples that are composed of 12 and 15 original features, respectively. Furthermore, the proposed model successfully obtained 0.26% and 0.22% MAPE for BMD and NASA-SSE data samples, respectively with a high correlation of about 99.97% within the actual and predicted data.
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